Long Presentations

Goals have long been recognised as important in user modelling and
personalisation. Surprisingly, little research has dealt with user model
representations for people's long term goals. This paper describes our
theoretically-grounded design of user models for long term goals; notably, the
theory points to the critical role of the user interface to this Goal Model, to
enable people to set, monitor and refine their models over the long term. We
report on a multi-study evaluation of the tightly coupled user model
representation and Goal Interface, based on a preliminary lab study (16
participants), and a field trial (14 participants), starting with the lab study
and then the in-the-wild use and the questionnaires. This provides multiple
sources of evidence to validate the usefulness of our Goal Model to represent
three long term health-related goals. It shows that the Goal Interface is
usable and aids people in setting their long term goals.

Aiming to ensure safety of operation to application providers and improve
the usability of human computer interactions during authentication, this paper
proposes a two-step personalization approach of user authentication tasks based
on individual differences in cognitive processing as follows: i) recommend a
textual or graphical user authentication mechanism based on the users'
cognitive styles of processing textual and graphical information, and ii)
recommend a standard or enhanced authentication key strength policy considering
the users' cognitive processing abilities. The proposed approach has been
applied in a four month ecological valid user study in which 137 participants
interacted with a personalized user authentication mechanism and policy based
on their cognitive characteristics. Initial results indicate that personalizing
the user authentication task based on human cognitive factors could provide a
viable solution for balancing the security and usability of authentication
mechanisms at the benefit of both application providers and end-users.

Recommender Systems need to deal with different types of users who represent
their preferences in various ways. This difference in user behaviour has a deep
impact on the final performance of the recommender system, where some users may
receive either better or worse recommendations depending, mostly, on the
quantity and the quality of the information the system knows about the user.
Specifically, the inconsistencies of the user impose a lower bound on the error
the system may achieve when predicting ratings for that particular user.
In this work, we analyse how the consistency of user ratings (coherence) may
predict the performance of recommendation methods. More specifically, our
results show that our definition of coherence is correlated with the so-called
magic barrier of recommender systems, and thus, it could be used to
discriminate between easy users (those with a low magic barrier) and difficult
ones (those with a high magic barrier). We report experiments where the rating
prediction error for the more coherent users is lower than that of the less
coherent ones. We further validate these results by using a public dataset,
where the magic barrier is not available, in which we obtain similar
performance improvements.

Mind wandering is a ubiquitous phenomenon where attention involuntary shifts
from task-related processing to task-unrelated thoughts. Mind wandering has
negative effects on performance, hence, intelligent interfaces that detect mind
wandering can intervene to restore attention to the current task. We
investigated the use of eye gaze and contextual cues to automatically detect
mind wandering during reading with a computer interface. Participants were
pseudo-randomly probed to report mind wandering instances while an eye tracker
recorded their gaze during a computerized reading task. Supervised machine
learning techniques detected positive responses to mind wandering probes from
gaze and context features in a user-independent fashion. Mind wandering was
predicted with an accuracy of 72% (expected accuracy by chance was 62%) when
probed at the end of a page and an accuracy of 59% (chance was 50%) when probed
in the midst of reading a page. Possible improvements to the detectors and
applications are discussed.

The social web is characterized by a wide variety of connections between
individuals and entities. A challenge for recommendation is to represent and
synthesize all useful aspects of a user's profile. Typically, researchers focus
on a limited set of relations (for example, person to person ties for user
recommendation or annotations in social tagging recommendation).
In this paper, we present a general approach to recommendation in
heterogeneous networks that can incorporate multiple relations in a weighted
hybrid. A key feature of this approach is the use of the metapath, an
abstraction of a class of paths in a network in which edges of different types
are traversed in a particular order. A user profile is therefore a composite of
multiple metapath relations. Compared to prior work with shorter metapaths, we
show that a hybrid composed of components using longer metapaths yields
improvements in recommendation diversity without loss of accuracy on social
tagging datasets.

Context has been recognized as an important factor in constructing
personalized recommender systems. However, most context-aware recommendation
techniques mainly aim at exploiting item-level contextual information for
modeling users' preferences, while few works attempt to detect more
fine-grained aspect-level contextual preferences. Therefore, in this article,
we propose a contextual recommendation algorithm based on user-generated
reviews, from where users' context-dependent preferences are inferred through
different contextual weighting strategies. The context-dependent preferences
are further combined with users' context-independent preferences for performing
recommendation. The empirical results on two real-life datasets demonstrate
that our method is capable of capturing users' contextual preferences and
achieving better recommendation accuracy than the related works.

Tag recommendation is a fundamental service in today's social annotation
systems, assisting users as they collect and annotate resources. Our previous
work has demonstrated the strengths of a linear weighted hybrid, which weights
and combines the results of simple components into a final recommendation.
However, these previous efforts treated each user the same. In this work, we
extend our approach by automatically discovering partitions of users. The user
partitioning hybrid learns a different set of weights for these user
partitions. Our rigorous experimental results show a marked improvement.
Moreover, analysis of the partitions within a dataset offers interesting
insights into how users interact with social annotations systems.

Location-based services usually recommend new locations based on the user's
current location or a given destination. However, human mobility involves to a
large extent routine behavior and visits to already visited locations. In this
paper, we show how daily and weekly routines can be modeled with basic
prediction techniques. We compare the methods based on their performance,
entropy and correlation measures. Further, we discuss how location prediction
for everyday activities can be used for personalization techniques, such as
timely or delayed recommendations.

In a tag-based recommender system, the multi-dimensional <user, item,
tag> correlation should be modeled effectively for finding quality
recommendations. Recently, few researchers have used tensor models in
recommendation to represent and analyze latent relationships inherent in
multi-dimensions data. A common approach is to build the tensor model,
decompose it and, then, directly use the reconstructed tensor to generate the
recommendation based on the maximum values of tensor elements. In order to
improve the accuracy and scalability, we propose an implementation of the
n-mode block-striped (matrix) product for scalable tensor reconstruction and
probabilistically ranking the candidate items generated from the reconstructed
tensor. With testing on real-world datasets, we demonstrate that the proposed
method outperforms the benchmarking methods in terms of recommendation accuracy
and scalability.

Thanks to social Web services, Web search engines have the opportunity to
afford personalized search results that better fit the user's information needs
and interests. To achieve this goal, many personalized search approaches
explore user's social Web interactions to extract his preferences and
interests, and use them to model his profile. In our approach, the user profile
is implicitly represented as a vector of weighted terms which correspond to the
user's interests extracted from his online social activities. As the user
interests may change over time, we propose to weight profiles terms not only
according to the content of these activities but also by considering the
freshness. More precisely, the weights are adjusted with a temporal feature. In
order to evaluate our approach, we model the user profile according to data
collected from Twitter. Then, we rerank initial search results accurately to
the user profile. Moreover, we proved the significance of adding a temporal
feature by comparing our method with baselines models that does not consider
the user profile dynamics.

In an ambience designed to adapt to the user's affective state, pervasive
technology should be able to decipher unobtrusively his underlying mood. Great
effort has been devoted to automatic punctual emotion recognition from visual
input. Conversely, little has been done to recognize longer-lasting affective
states, such as mood. Taking for granted the effectiveness of emotion
recognition algorithms, we go one step further and propose a model for
estimating the mood of an affective episode from a known sequence of punctual
emotions. To validate our model experimentally, we rely on the human
annotations of the well-established HUMAINE database. Our analysis indicates
that we can approximate fairly accurately the human process of summarizing the
emotional content of a video in a mood estimation. A moving average function
with exponential discount of the past emotions achieves mood prediction
accuracy above 60%.

Context-aware systems (CAS) that collect personal information are a general
trend. This leads to several privacy considerations, which we outline in this
paper. We present as use-case the SWELL system, which collects information from
various contextual sensors to provide support for well-being at work. We
address privacy from two perspectives: 1) the development point of view, in
which we describe how to apply 'privacy by design', and 2) a user study, in
which we found that providing detailed information on data collection and
privacy by design had a positive effect on trust in our CAS. We also found that
the attitude towards using our CAS was related to personal motivation, and not
related to perceived privacy and trust in our system. This may stress the
importance of implementing privacy by design to protect the privacy of the
user.

Neighbourhood-based collaborative filtering recommenders exploit the common
ratings among users to identify a user's most similar neighbours. It is known
that decisions made on a naive computation of user similarity are unreliable,
because the number of co-ratings varies strongly among users. In this paper, we
formalize the notion of reliable similarity between two users and propose a
method that constructs a user's neighbourhood by selecting only those users
that are reliably similar to her. Our method combines a statistical test and
the notion of a baseline user. We report our results on typical benchmark
datasets.

A large body of work in the information retrieval area has highlighted that
relevance is a complex and a challenging concept. The underlying complexity
appears mainly from the fact that relevance is estimated by considering
multiple dimensions and that most of them are subjective since they are
user-dependent. While the most used dimension is topicality, recent works risen
particularly from personalized information retrieval have shown that personal
preferences and contextual factors such as interests, location and task
peculiarities have to be jointly considered in order to enhance the computation
of document relevance. To answer this challenge, the commonly used approaches
are based on linear combination schemes that rely basically on the
non-realistic independency property of the relevance dimensions. In this paper,
we propose a novel fuzzy-based document relevance aggregation operator able to
capture the user's importance of relevance dimensions as well as information
about their interaction. Our approach is empirically evaluated and relies on
the standard TREC contextual suggestion dataset involving 635 users and 50
contexts. The results highlight that accounting jointly for individual
differences toward relevance dimension importance as well as their interaction
introduces a significant improvement in the retrieval performance.

Learning from worked examples has been shown to be superior to unsupported
problem solving when first learning in a new domain. Several studies have found
that learning from examples results in faster learning in comparison to tutored
problem solving in Intelligent Tutoring Systems. We present a study that
compares a fixed sequence of alternating worked examples and tutored problem
solving with a strategy that adaptively decides how much assistance the student
needs. The adaptive strategy determines the type of task (a worked example, a
faded example or a problem to be solved) based on how much assistance the
student received in the previous problem. The results show that students in the
adaptive condition learnt significantly more than their peers who were
presented with a fixed sequence of worked examples and problems.

Information visualization systems have traditionally followed a
one-size-fits-all paradigm with respect to their users, i.e., their design is
seldom personalized to the specific characteristics of users (e.g. perceptual
abilities) or their tasks (e.g. task difficulty). In view of creating
information visualization systems that can adapt to each individual user and
task, this paper provides an analysis of user eye gaze data aimed at
identifying behavioral patterns that are specific to certain user and task
groups. In particular, the paper leverages the sequential nature of user eye
gaze patterns through differential sequence mining, and successfully identifies
a number of pattern differences that could be leveraged by adaptive information
visualization systems in order to automatically identify (and consequently
adapt to) different user and task characteristics.

This article reports on a modification of the user-kNN algorithm that
measures the similarity between users based on the similarity of text reviews,
instead of ratings. We investigate the performance of text semantic similarity
measures and we evaluate our text-based user-kNN approach by comparing it to a
range of ratings-based approaches in a ratings prediction task. We do so by
using datasets from two different domains: movies from RottenTomatoes and Audio
CDs from Amazon Products. Our results show that the text-based userkNN
algorithm performs significantly better than the ratings-based approaches in
terms of accuracy measured using RMSE.

In the culture domain, questionnaires are often used to obtain profiles of
users for adaptation. Creating questionnaires requires subject matter experts
and diverse content, and often does not scale to a variety of cultures and
situations. This paper presents a novel approach that is inspired by
crowdwisdom and takes advantage of freely available structured linked data. It
presents a mechanism for extracting culturally-related facts from DBpedia,
utilised as a knowledge source in an interactive user modelling system. A user
study, which examines the system usability and the accuracy of the resulting
user model, demonstrates the potential of using DBpedia for generating
culture-related user modelling questionnaires and points at issues for further
investigation.

We present an analysis of user gaze data to understand if and how user
characteristics impact visual processing of bar charts in the presence of
different highlighting interventions designed to facilitate visualization
usage. We then link these results to task performance in order to provide
insights on how to design user-adaptive information visualization systems. Our
results show how the least effective intervention manifests itself as a
distractor based on gaze patterns. The results also identify specific
visualization regions that cause poor task performance in users with low values
of certain cognitive measures, and should therefore be the target of
personalized visualization support.

Attacks on Collaborative Filtering Recommender Systems (RS) can bias
recommendations, potentially causing users to distrust results and the overall
system. Attackers constantly innovate, and understanding the implications of
novel attack vectors on system robustness is important for designers and
operators. Foundational research on attacks in RSs studied attack user profiles
based on straightforward models such as random or average ratings data. We are
studying a novel category of attack based explicitly on measures of influence,
in particular the potential impact of high-influence power users. This paper
describes our approach to generate synthetic attack profiles that emulate
influence characteristics of real power users, and it studies the impact of
attack vectors that use synthetic power user profiles. We evaluate both the
quality of synthetic power user profiles and the effectiveness of the attack,
on both user-based and matrix-factorization-based recommender systems. Results
show that synthetic user profiles that model real power users are an effective
way of attacking collaborative recommender systems.

A support vector machine is trained to classify the Five Factor personality
of writers of free text. Writers are classified for each of the five
personality dimensions as high/low with the mean personality score for each
dimension used for the dividing point. Writers are also separately classified
as high/medium/low with division points at one standard deviation above and
below mean. The two-class average accuracy using 5-fold cross validation of
80.6% is much better than the baseline (pick most likely class) accuracy of
50%, but the 3-class accuracy is only slightly better (7.4%) than baseline
because most writers fall into the medium class due to the normal distribution
of personality values. Features include bag of words, essay length, word
sentiment, negation count and part-of-speech n-grams. The consistently positive
contribution of POS n-grams (averaging 4.8% and 5.8% for the 2/3 class cases)
is analyzed in detail. The information gain for the most predictive features
for each of the five personality dimensions are presented and discussed.

Critiquing-based recommender systems offer users a conversational paradigm
to provide their feedback, named critiques, during the process of viewing the
current recommendation. In this way, the system is able to learn and adapt to
the users' preferences more precisely so that better recommendation could be
returned in the subsequent iteration. Moreover, recent works on
experience-based critiquing have suggested the power of improving the
recommendation efficiency by making use of relevant sessions from other users'
histories so as to save the active user's interaction effort. In this paper, we
present a novel approach to processing the history data and apply it to the
compound critiquing system. Specifically, we develop a history-aware
collaborative compound critiquing method based on preference-based compound
critique generation and graph-based similar session identification. Through
experiments on two data sets, we validate the outperforming efficiency of our
proposed method in comparison to the other experience-based methods. In
addition, we verify that incorporating user histories into compound critiquing
system can be significantly more effective than the corresponding unit
critiquing system.

Question Answering platforms are becoming an important repository of
crowd-generated knowledge. In these systems a relatively small subset of users
is responsible for the majority of the contributions, and ultimately, for the
success of the Q/A system itself. However, due to built-in incentivization
mechanisms, standard expert identification methods often misclassify very
active users for knowledgable ones, and misjudge activeness for expertise. This
paper contributes a novel metric for expert identification, which provides a
better characterisation of users' expertise by focusing on the quality of their
contributions. We identify two classes of relevant users, namely sparrows and
owls, and we describe several behavioural properties in the context of the
StackOverflow Q/A system. Our results contribute new insights to the study of
expert behaviour in Q/A platforms, that are relevant to a variety of contexts
and applications.

Short Presentations

Recent years have seen growing interest in automated goal recognition. In
user-adaptive systems, goal recognition is the problem of recognizing a user's
goals by observing the actions the user performs. Models of goal recognition
can support student learning in intelligent tutoring systems, enhance
communication efficiency in dialogue systems, or dynamically adapt software to
users' interests. In this paper, we describe an approach to goal recognition
that leverages Markov Logic Networks (MLNs) -- a machine learning framework
that combines probabilistic inference with first-order logical reasoning -- to
encode relations between problem-solving goals and discovery events,
domain-specific representations of user progress in narrative-centered learning
environments. We investigate the impact of discovery event representations on
goal recognition accuracy and efficiency. We also investigate the
generalizability of discovery event-based goal recognition models across two
corpora from students interacting with two distinct narrative-centered learning
environments. Empirical results indicate that discovery event-based models
outperform previous state-of-the-art approaches on both corpora.

The application of educational data mining (EDM) techniques to interactive
learning software is increasingly being used to broaden the range of constructs
typically incorporated in student models, moving from traditional assessment of
student knowledge to the assessment of engagement, affect, strategy, and
metacognition. Researchers are also broadening the range of environments within
which these constructs are assessed. In this study, we develop sensor-free
affect detection for EcoMUVE, an immersive multi-user virtual environment that
teaches middle-school students about casualty in ecosystems. In this study,
models were constructed for five different educationally-relevant affective
states (boredom, confusion, delight, engaged concentration, and frustration).
Such models allow us to examine the behaviors most closely associated with
particular affective states, paving the way for the design of adaptive
personalization to improve engagement and learning.

Mind-maps have been widely neglected by the information retrieval (IR)
community. However, there are an estimated two million active mind-map users,
who create 5 million mind-maps every year, of which a total of 300,000 is
publicly available. We believe this to be a rich source for information
retrieval applications, and present eight ideas on how mind-maps could be
utilized by them. For instance, mind-maps could be utilized to generate user
models for recommender systems or expert search, or to calculate relatedness of
web-pages that are linked in mind-maps. We evaluated the feasibility of the
eight ideas, based on estimates of the number of available mind-maps, an
analysis of the content of mind-maps, and an evaluation of the users'
acceptance of the ideas. We concluded that user modelling is the most promising
application with respect to mind-maps. A user modelling prototype -- a
recommender system for the users of our mind-mapping software Docear -- was
implemented, and evaluated. Depending on the applied user modelling approaches,
the effectiveness, i.e. click-through rate on recommendations, varied between
0.28% and 6.24%. This indicates that mind-map based user modelling is
promising, but not trivial, and that further research is required to increase
effectiveness.

The increasing popularity of social networks has encouraged a large number
of significant research works on community detection and user recommendation.
The idea behind this work is that taking into account peculiar users' attitudes
(i.e., sentiments, opinions or ways of thinking) toward their own interests can
bring benefits in performing such tasks. In this paper we describe (i) a novel
method to infer sentiment-based communities without the requirement of
obtaining the whole social structure, and (ii) a community-based approach to
user recommendation. We take advantage of the SVO
(sentiment-volume-objectivity) user profiling and the Tanimoto similarity to
evaluate user similarity for each topic. Afterwards we employ a clustering
algorithm based on modularity optimization to find densely connected users and
the Adamic-Adar tie strength to finally suggest the most relevant users to
follow. Preliminary experimental results on Twitter reveal the benefits of our
approach compared to some state-of-the-art user recommendation techniques.

The relevance of contextual factors that adapt in-car recommendations to the
driver's current situation is not yet fully understood. This paper presents a
field study that has been conducted in order to identify relevant contextual
factors of in-car parking lot recommender systems. Surprisingly, most
contextual factors examined, i.e., weather, luggage, and traffic conditions,
did not have a significant effect on the parking lot decision in the conducted
field study. Only the urgency of the trip and the willingness to walk have
significant effects on the decision outcome. Therefore, automobile
manufacturers should focus on understanding the relevance of different
contextual factors when developing user models for in-car recommender systems.

Smart energy systems are able to support users in saving energy by
controlling devices, such as lights or displays, depending on context
information, such as the brightness in a room or the presence of users.
However, proactive decisions should also match the users' preferences to
maintain users' trust in the system. Wrong decisions could negatively influence
users' acceptance of a system and at worst could make them abandon the system.
In this paper, a trust-based model, called User Trust Model (UTM), for
automatic decision-making is proposed, which is based on Bayesian Networks. The
UTM's construction, the initialization with empirical data gathered in an
online survey, and its integration in an office setting are described.
Furthermore, the results of a user study investigating users' experience and
acceptance are presented.

When modeling student knowledge and predicting student performance, adaptive
educational systems frequently rely on content models that connect learning
content (i.e., problems) with its underlying domain knowledge (i.e., knowledge
components, KCs) required to complete it. In some domains, such as programming,
the number of KCs associated with advanced learning contents is quite large. It
complicates modeling due to increasing noise and decreases efficiency. We argue
that the efficiency of modeling and prediction in such domains could be
improved without the loss of quality by reducing problems content models to a
subset of most important KCs. To prove this hypothesis, we evaluate several KC
reduction methods varying reduction size by assessing the prediction
performance of Knowledge Tracing and Performance Factor Analysis. The results
show that the predictive performance using reduced content models can be
significantly better than using original one, with extra benefits of reducing
time and space.

In this paper we present an analysis of improvements to a web-based
Graphical User Interface (GUI) for health surveillance systems. Such systems
are designed to provide means to detect and suggest outbreaks and corresponding
information about them from both formal (e.g., hospital reports) and informal
(e.g., news sites) sources. However, despite the availability of different such
systems, few studies have been carried out to discuss the elements of the
system's GUI and how it can support users in their tasks. To this end, we
investigate techniques for adapting, structuring and browsing information in an
intuitive and friendly way to the user, focusing on a transition from a static
to a dynamic adapted web experience. We conduct a case study with health
surveillance experts where we present a case for recommendations matching the
user's preferences within a system and discuss improvements to the presented
GUI. We discuss improvements in the light of the feedback provided by these
users, proposing how adapted elements of a GUI can be used to improve the user
experience in a surveillance task.

At the beginning of every course, it can be expected that several students
have some syllabus knowledge. For efficiency in learning systems, and to combat
student frustration and boredom, it is important to quickly uncover this latent
knowledge. This enables students to begin new learning immediately. In this
paper we compare two algorithms used to achieve this goal, both based on the
theory of Knowledge Spaces. Simulated students were created with appropriate
answering patterns based on predefined latent knowledge from a subsection of a
real course. For each student, both algorithms were applied to compare their
efficiency and their accuracy. We examine the trade-off between both sets of
outcomes, and conclude with the merits and constraints of each algorithm.

The centralized gathering and processing of user information made by
traditional recommender systems can lead to user information exposure,
violating her privacy. Client-side personalization methods have been created as
a mean for avoiding privacy risks. Motivated by limiting the exposure of user
private information, we explore the use of a client-side hybrid recommender
system placed on the online learning setting. We propose a prediction model
based on an ensemble blender of an online matrix factorization CF model and a
logistic regression model trained on item metadata with a probabilistic feature
inclusion strategy. The final prediction is a blend of the two models on a
weighted regret approach. We validate our approach with the Movielens 10M
dataset.

The effectiveness of content-based recommendation strategies tremendously
depends on the representation formalism adopted to model both items and user
profiles. As a consequence, techniques for semantic content representation
emerged thanks to their ability to filter out the noise and to face with the
issues typical of keyword-based representations. This article presents
Contextual eVSM (C-eVSM), a content-based context-aware recommendation
framework that adopts a novel semantic representation based on distributional
models and entity linking techniques. Our strategy is based on two insights:
first, entity linking can identify the most relevant concepts mentioned in the
text and can easily map them with structured information sources, easily
triggering some inference and reasoning on user preferences, while
distributional models can provide a lightweight semantics representation based
on term co-occurrences that can bring out latent relationships between concepts
by just analyzing their usage patterns in large corpora of data.
The resulting framework is fully domain-independent and shows better
performance than state-of-the-art algorithms in several experimental settings,
confirming the validity of content-based approaches and paving the way for
several future research directions.

IntelWiki: Recommending Resources to Help Users Contribute to Wikipedia

We describe an approach to facilitating user-generated content within the
context of Wikipedia. Our approach, embedded in the IntelWiki prototype, aims
to make it easier for users to create or enhance the free-form text in
Wikipedia articles by: i) recommending potential reference materials, ii)
drawing the users' attention to key aspects of the recommendations, and iii)
allowing users to consult the recommended materials in context. A laboratory
evaluation with 16 novice Wikipedia editors revealed that, in comparison to the
default Wikipedia design, IntelWiki's approach has positive impacts on editing
quantity and quality, and perceived mental load.

Personalisation for cultural heritage aims at delivering to visitors the
right stories at the right time. Our endeavour to determine which features to
use for adaptation starts from acknowledging what forms of personalisation
curators value as most meaningful. Working in collaboration with curators we
have explored the different features that must be taken into account: some are
related to the content (multiple interpretation layers), others to the context
of delivery (where and when), but some are idiosyncratic ("match my mood",
"something that is relevant to my life"). The findings reveal that a
sustainable personalization needs to accurately balance: (i) support to
curators in customising stories to different visitors; (ii) algorithms for the
system to dynamically model aspects of the visit and instantiate the correct
behaviour; and (iii) an active role for visitors to choose the type of
experience they would like to have today.

We define job interviews as a domain of interaction that can be modelled
automatically in a serious game for job interview skills training. We present
four types of studies: (1) field-based human-to-human job interviews, (2)
field-based computer-mediated human-to-human interviews, (3) lab-based wizard
of oz studies, (4) field-based human-to-agent studies. Together, these
highlight pertinent questions for the user modelling field as it expands its
scope to applications for social inclusion. The results of the studies show
that the interviewees suppress their emotional behaviours and although our
system recognises automatically a subset of those behaviours, the modelling of
complex mental states in real-world contexts poses a challenge for the
state-of-the-art user modelling technologies. This calls for the need to
re-examine both the approach to the implementation of the models and/or of
their usage for the target contexts.

In this paper, we present an approach for predicting users' level of
engagement from nonverbal cues within a game environment. We use a data corpus
collected from 28 participants (152 minutes of video recording) playing the
popular platform game Super Mario Bros. The richness of the corpus allows
extraction of several visual and facial expression features that were utilised
as indicators of players' affects as captured by players' self-reports.
Neuroevolution preference learning is used to construct accurate models of
player experience that approximate the relationship between extracted features
and reported engagement. The method is supported by a feature selection
technique for choosing the relevant subset of features. Different setup
settings were implemented to analyse the impact of the type of the features and
the position of the extraction window on the modelling accuracy. The results
obtained show that highly accurate models can be constructed (with accuracies
up to 96.82%) and that players' nonverbal behaviour towards the end of the game
is the most correlated with engagement. The framework presented is part of a
bigger picture where the generated models are utilised to tailor content
generation to a player's particular needs and playing characteristics.

The shift from the originally English-language-dominated web towards a truly
global world wide web has generated a pressing need to develop novel solutions
that address multilingual user diversity. In particular, many web users today
are polyglots, i.e. they are proficient in more than one language. However,
little is known about the browsing and search habits of such users, and even
less about how to best assist their multilingual behaviors through appropriate
systems and tools. In order to gain a better understanding, this paper presents
a survey of 385 polyglot web users, focusing specifically on the relationship
between multiple language proficiency and browsing/search language choice.
Results from the survey indicate that polyglot users make significant use of
multiple languages during their daily browsing and searching, and that
contextual factors such as language proficiency, usage purpose, and topic
domain have a significant influence on their language choice and frequency. The
paper provides a detailed analysis regarding each of these factors, and offers
insights about how to support multilingual users through novel Personalized
Multilingual Information Access systems.

Recommender systems use nowadays more and more data about users and items as
part of the recommendation process. The availability of auxiliary data, going
beyond the mere user/item data, has the potential to improve recommendations.
In this work we examine the contribution of two types of social auxiliary data
-- namely, tags and friendship links -- to the accuracy of a graph-based
recommender. We measure the impact of the availability of auxiliary data on the
recommendations using features extracted from both the auxiliary and the
original data. The evaluation shows that the social auxiliary data improves the
accuracy of the recommendations, and that the greatest improvement is achieved
when graph features mirroring the nature of the auxiliary data are extracted by
the recommender.

Traditional Collaborative Filtering algorithms for recommendation are
designed for stationary data. Likewise, conventional evaluation methodologies
are only applicable in offline experiments, where data and models are static.
However, in real world systems, user feedback is continuously being generated,
at unpredictable rates. One way to deal with this data stream is to perform
online model updates as new data points become available. This requires
algorithms able to process data at least as fast as it is generated. One other
issue is how to evaluate algorithms in such a streaming data environment. In
this paper we introduce a simple but fast incremental Matrix Factorization
algorithm for positive-only feedback. We also contribute with a prequential
evaluation protocol for recommender systems, suitable for streaming data
environments. Using this evaluation methodology, we compare our algorithm with
other state-of-the-art proposals. Our experiments reveal that despite its
simplicity, our algorithm has competitive accuracy, while being significantly
faster.

When the Question is Part of the Answer: Examining the Impact of Emotion
Self-reports on Student Emotion

A variety of methodologies have been put forth to assess students' affective
states as they use interactive learning environments (ILEs) and intelligent
tutoring systems (ITS), such as classroom observations and subjective coding,
self-coding by students after replays, as well as self-reports of student
emotion as students are using the learning environment. Still, it is unclear
what the disadvantages of each methodology are. In particular, does measuring
affect by asking students to self-report alter student affect itself? The
following work explores this question of how self-reports themselves can bias
affective states, within one particular tutoring system, Wayang Outpost.

Keywords: affect; assessment; modeling of emotions

Doctoral Consortium

With the explosive growth of information available in the web, locating
needed and relevant information remains a difficult task, whether the
information is textual or visual. Although information-retrieval algorithms
have improved greatly in retrieving relevant information, exploratory
information-seeking still remains difficult due to its inherently open-ended
and dynamic nature. Modeling the user behavior and predicting dynamically
changing information-needs in exploratory search is hard. Over the past decade
there has been increasing attention on rich user interfaces, retrieval
techniques, and studies of exploratory search. However, existing work does not
yet support the dynamic aspects of exploratory search. The objective of this
research is to understand how user interaction modeling can be applied to
provide better support in exploratory information-seeking.

A challenge of Context-Aware Recommender Systems (CARSs) is the cold-start
problem, i.e., the usual poor recommendation of new items to new users in new
contextual situations. In this research, we aim at solving this problem by
developing a switching hybrid CARS, which exploits different context-aware
recommendation techniques, each of which has its own strengths and weaknesses,
and switches between these techniques depending on the current recommendation
situation (i.e., new user, new item and/or new context).

Mobile recommender systems provide personalized recommendations to help deal
with today's information overload. However, due to spatial limitations in
mobile interfaces and uncertainty of the user's preferences in the beginning,
the improvement of the user experience remains one of the main challenges when
designing these systems and has not been investigated thoroughly. This paper
describes the aim and progress of the author's PhD studies on the user
interaction, usability and accuracy of mobile recommender systems. The approach
aims to combine different user interaction methods with context-awareness to
allow user-friendly personalized mobile recommendations.

Museums, as cultural heritage sites, have long been a primary showground for
the exploration of new technologies. Recent new directions for research in this
field, have concerned themselves with 1) expanding the on-site visit with prior
and post experiences, primarily at a desktop computer at home, but not
necessarily; 2) expanding the visit from a onetime experience to an experience
that may repeat itself multiple times over a lifetime, including the reuse of
personal information elicited from experience gained onsite (e.g. a user model)
for providing personalized experience at multiple sites. The proposed third new
direction for research in this field, the one which is focused on is: examining
how to enhance other experiences outside the museum site, based on experiences
at the museum site. By doing this one can begin to connect our cultural
heritage experiences to our "daily" lives.

Personality assessment can be used to predict subjects' use of products and
services, thriving in academic programs, and performance in work environments.
To avoid the costs and inconvenience of administering personality
questionnaires, researchers have inferred author personality from their
writings. Extending such methods will enable marketing, interface adaptation,
and a variety of data mining applications. The proposed program of research
examines elements of syntax, addressing the following questions: does authors'
usage of English grammatical structures reflect their personalities? What
methodology extracts and predicts personality from grammar usage? Key to this
approach is the use of locally defined grammatical structures as described by
Part of Speech n-grams.